One program, multiple training sites: does site of family medicine training influence professional practice location?
Bibliographic record
Abstract
INTRODUCTION: Numerous strategies have been suggested to increase recruitment of family physicians to rural communities and smaller regional centers. One approach has been to implement distributed postgraduate education programs where trainees spend substantial time in such communities. The purpose of the current study was to compare the eventual practice location of family physicians who undertook their postgraduate training through a single university but who were based in either metropolitan or distributed, non-metropolitan communities. METHODS: Since 1998, the Department of Family Practice at the University of British Columbia in Canada has conducted an annual survey of its residents at 2, 5, and 10 years after completion of training. The authors received Ethics Board approval to use this anonymized data to identify personal and educational factors that predict future practice location. RESULTS: The overall response rate was 45%. At 2 years (N=222), residents trained in distributed sites were 15 times more likely to enter practice in rural communities, small towns and regional centers than those who trained in metropolitan teaching centers. This was even more predictive for retention in non-urban practice sites. Among the subgroup of physicians who remained in a single practice location for more than a year preceding the survey, those who trained in smaller sites were 36 times more likely to choose a rural or regional practice setting. While the vast majority of those trained in metropolitan sites chose an urban practice location, a subgroup of those with some rural upbringing were more likely to practice in rural or regional settings. Trainees from distributed sites considered themselves more prepared for practice regardless of ultimate practice location. CONCLUSIONS: Participation in a distributed postgraduate family medicine training site is an important predictor of a non-urban practice location. This effect persists for 10 years after completion of training and is independent of other predictors of non-urban practice including gender, rural upbringing, and rural undergraduate training. It is hypothesized that this is due not only to a curriculum that supports preparedness for this type of practice but also to opportunities to develop personal and professional roots in these communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".